Neural adaptive deep brain stimulation (NaDBS) using beta band power as a biomarker is currently being investigated as a therapy for Parkinson's disease (PD). These systems modulate DBS amplitude in response to beta power fluctuations resulting from medication use or the underlying disease. While NaDBS has been shown to be a potentially effective treatment for PD, it may not be well suited to capture and respond to the stochastic symptoms of the disease. External kinematic sensors can reliably capture these stochastic symptoms and be used as an additional input to make stimulation decisions. We demonstrate a distributed, hybrid controller that can analyze kinematic inputs from external sensors and neural inputs to make stimulation decisions on a second-by-second basis. The hybrid controller simultaneously streams kinematic data from wearable sensors on the shanks of a participant and neural data from an implantable neurostimulator into a PC in the loop. These data streams are time synchronized on a second-bysecond basis and the two data streams are analyzed. When a freezing event is detected in the kinematic data, the controller rapidly increases stimulation to a predetermined maximum intensity (Imax). When no freezing event is detected, or if the controller is uncertain if a freezing event occurred, the controller modulates stimulation slowly based on beta band power received from the neural input. A benchtop demonstration of this system shows that this controller architecture is feasible. The mean overall system latency was 311ms (SD = 102ms) and the mean latency across the PC in the loop portion was 178ms (SD = 86ms), below the second-by-second basis that the controller operates on. This proof-of-concept hybrid controller can be used in the future to develop DBS control policies with multiple additional inputs.